Learning to be thoughtless: Social norms and individual computation

Joshua M. Epstein

Research output: Contribution to journalArticle

Abstract

This paper extends the literature on the evolution of norms with an agent-based model capturing a phenomenon that has been essentially ignored, namely that individual thought - or computing - is often inversely related to the strength of a social norm. Once a norm is entrenched, we conform thoughtlessly. In this model, agents learn how to behave (what norm to adopt), but - under a strategy I term Best Reply to Adaptive Sample Evidence - they also learn how much to think about how to behave. How much they are thinking affects how they behave, which - given how others behave - affects how much they think. In short, there is feedback between the social (inter-agent) and internal (intra-agent) dynamics. In addition, we generate the stylized facts regarding the spatio-temporal evolution of norms: local conformity, global diversity, and punctuated equilibria.

Original languageEnglish (US)
Pages (from-to)9-24
Number of pages16
JournalComputational Economics
Volume18
Issue number1
DOIs
StatePublished - Aug 1 2001

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Keywords

  • Agent-based computational economics
  • Evolution of norms

ASJC Scopus subject areas

  • Economics, Econometrics and Finance (miscellaneous)
  • Computer Science Applications

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